Building Batch Data Pipelines on Google Cloud

Modern businesses rely on clean, reliable, and timely data to make critical decisions. Building efficient batch pipelines on cloud infrastructure is the key to transforming raw data into actionable business intelligence. In this text-based course, you will learn how to design, build, and optimize batch data workflows on Google Cloud. You will progress from foundational cloud data concepts to implementing scalable data transformations and managing automated pipeline workflows. What you'll learn: - Understand fundamental batch data processing concepts and Google Cloud architecture. - Build scalable data pipelines using Dataflow and Apache Beam. - Process large-scale datasets with serverless Spark on Dataproc. - Orchestrate and schedule workflows efficiently using modern Cloud Composer and Airflow patterns. - Optimize data storage and query performance using BigQuery partitioning and clustering. The course starts with essential data engineering terminology and core Google Cloud services before moving into hands-on pipeline development. You will read comprehensive explanations, study clean code snippets, and review real-world design patterns for data transformations. This course is designed for aspiring data engineers, analysts, and developers who are new to cloud data pipelines. No prior cloud engineering experience is required, though a basic understanding of SQL and Python is helpful. Start reading today to build reliable, production-ready batch pipelines on Google Cloud.